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A Projection Algorithm for Regression with Collinearity

  • Peter Filzmoser
  • Christophe Croux
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Principal component regression (PCR) is often used in regression with multicollinearity. Although this method avoids the problems which can arise in the least squares (LS) approach, it is not optimized with respect to the ability to predict the response variable. We propose a method which combines the two steps in the PCR procedure, namely finding the principal components (PCs) and regression of the response variable on the PCs. The resulting method aims at maximizing the coefficient of determination for a selected number of predictor variables, and therefore the number of predictor variables can be reduced compared to PCR. An important feature of the proposed method is that it can easily be robustified using robust measures of correlation.

Keywords

Predictor Variable Ridge Regression Stepwise Selection Principal Component Regression Less Square Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Peter Filzmoser
    • 1
  • Christophe Croux
    • 2
  1. 1.Department of Statistics and Probability TheoryVienna University of TechnologyViennaAustria
  2. 2.Department of Applied EconomicsK.U.LeuvenLeuvenBelgium

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